Ce cours constitue la seconde partie d'un enseignement consacré aux bases théoriques et pratiques des systèmes d’information géographique. - Il propose une introduction aux systèmes d’information géographique qui ne requiert pas de connaissances préalables en informatique. - Il donne la possibilité d’acquérir rapidement les notions de base qui vous permettent de créer des bases de données spatiales et de fabriquer des cartes géographiques. - Il s’agit d’un cours pratique qui repose sur l’utilisation de logiciels libres, notamment QGIS. Lors de la première partie du cours, vous avez exploré les principes de base de la numérisation du territoire et du stockage des géodonnées. Vous avez notamment appris à : - Caractériser des objets et des phénomènes spatiaux (modélisation du territoire) du point de vue de leur positionnement dans l’espace (systèmes de coordonnées et projections, relations spatiales) et en fonction de leur nature intrinsèque (mode objet ou vecteur vs. mode image ou raster); - Utiliser diverses méthodes d’acquisition de données (mesure directe, géoréférencement d’images, digitalisation, source de données existantes, etc.); - Utiliser divers modes de stockage des géodonnées (fichiers simples et bases de données relationnelles); - Utiliser des outils de modélisation des données pour décrire et implémenter une base de données; - Créer des requêtes dans un langage d’interrogation et de manipulation des données. La seconde partie du cours porte sur les méthodes d'analyse spatiale et les techniques de représentation de l'information géoréférencée. Vous apprendrez notamment à: - Analyser les propriétés spatiales de variables discrètes, par exemple en quantifiant l’autocorrélation spatiale; - Travailler avec des variables continues (échantillonnage, interpolation et construction de courbes d’isovaleurs) - Utiliser les modèles numériques d'altitude et leurs dérivées (pente, orientation, etc.); - Utiliser des techniques de superposition des géodonnées; - Produire des documents cartographiques selon les règles de la sémiologie graphique; - Explorer d’autres formes de représentation spatiale (cartographie interactive sur internet, représentations 3D, et réalité augmentée). La page https://www.facebook.com/moocsig fournit un forum interactif pour les participants à ce cours.
Variables dérivées (2)
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En provenance du cours de École Polytechnique Fédérale de Lausanne
Systèmes d’Information Géographique - Partie 2
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À partir de la leçon
Modèles numériques d'altitude
Le relief est le facteur révélateur et explicatif prépondérant des phénomènes survenus à la surface de la Terre. Il a fortement conditionné et orienté l’appropriation de l’espace géographique par les sociétés humaines. Selon ses propriétés, les règnes animal et végétal trouvent des conditions favorables à leur établissement et à leur développement. Il occupe, de ce fait, une place majeure en analyse spatiale et dans toute discipline dont l’objet concerne les activités humaines ou des phénomènes naturels. Ce chapitre présente un bref historique des modes d’acquisition de l’altitude, puis expose les algorithmes et leurs propriétés permettant de dériver les indicateurs descriptifs du relief.
Rencontrer les enseignants
Marc Soutter
Laboratoire de Systèmes d'Information Géographique
Stéphane Joost
Laboratoire de systèmes d'information géographique
Fernand Koffi KouaméProfesseur
Centre Universitaire de Recherche et d'Application en Télédétection (CURAT) - Université Félix HOUPHOUET-BOIGNY d'Abidjan-Cocody (Côte d'Ivoire)
Amadou Sall
Département de Géomatique - Centre de Suivi Ecologique (CSE), Sénégal
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[MUSIC] Hello again, and welcome
to our second lesson on variables derived from digital elevation models
in which we will introduce some important parameters for hydrological studies,
notably how to calculate flow lines and the delimit catchment areas.
In the last lesson, we introduced the most common variables that are
are derived from digital elevation models. As Amadou Sall indicated,
the goal of this final lesson on DEMs is to introduce another
class of thematic variables.
In many disciplines, elevation is an especially important variable that can be used to
identify zones that - based on their derived topographic characteristics,
like their slope, curvature or aspect – can either be favored or avoided.
But digital elevation models possess other properties that can be exploited
to create other useful variables.
In this lesson we will show you how they are calculated and how they can be adapted for use
in different applications, particularly in the context of natural hazard assessment.
We will begin by introducing analyses that are based on lines of sight;
these include viewshed analysis,
drop shadows and projected shadows, and the calculation of solar irradiance.
We will then move on to consider derived variables that are used in hydrology such as
drainage lines, and we will also see how GIS can be used to delimit drainage basins.
We will finish by showing you how some indicators can be
used to assess natural dangers and what
role they can play in the public health domain.
[MUSIC] There are a few different approaches
that can be used to generate lines of sight can from a digital elevation model.
A line of sight is effectively a straight line that connects two points
and is constituted by all of the pixels that are located along it.
The first approach is called viewshed analysis,
and is used to determine the entire area that is visible
from a given vantage point, given here in red.
This technique analyses all lines of sight with reference
to the relief in question. All possible viewing directions are considered.
In this analysis, a single vantage point is considered
and all visible terrain, given in yellow
is distinguished from the areas that are not – in white.
This function is often used in impact assessments;
for example, if we want to build a new construction,
we can determine the entire area that will be visible from it once built.
In the opposite sense, viewshed analysis could also be
used to determine the visual impact that a wind turbine will have on the surrounding area
That is: from which points will the turbine be visible?
This type of analysis is also frequently used to evaluate the reception zones
that would be created by installing a telephone antenna in a given location.
In the figure in the lower left, the transmitter is placed at the center of the red circle,
and the radius indicates the strength of the installation.
Areas shown in brown are not within the
transmitters field of view and would receive poor reception.
Hillshade models can be created from digital terrain models and represent
elevation data in a format that gives us the impression of relief.
Hillshade models are created by generating lines of sight from a specific position
in order to simulate a light source.
Relief is simulated by modifying the shade of grey depending on the surface orientation with respect to
the light source. Zones that are oriented in the opposite direction of the light
source and those that exhibit an inclination superior to the light source are greyed out.
This shadow is a drop shadow.
If we instead simulate the true shadow that would be created by
the relief it is called the projected shadow.
Solar potential is equal to the irradiation received by a surface assuming a cloudless sky.
It is modeled using a similar approach to that used
for the creation of a hillshade model,
but it considers all possible projected shadows.
Each grid cell corresponds to one point on the DTM,
and it the amount of solar irradiation that it receives
is dependent on the height of the sun, the transparency of the atmosphere,
the surface slope and aspect as well as the structure of the horizon line.
The result is given in Kilowatt per hour per meter squared.
The function must first define the 360-degree horizon for every point in the DTM,
as in this illustration shown on the right of the screen.
Next, specific algorithms, such as those in the freely-available SAGA program,
then calculate the solar potential received
by each pixel over a specified time period.
For each geographic location, you must specify at which point,
and at what time, the sun rises, and must also define its
trajectory across the sky, and the time that it sets.
The result of these algorithms is a uniformly-spaced grid, where each pixel value indicates
the amount of potential solar energy that would be received at that precise location.
Here, on the left we have a 25m resolution digital elevation model
model that we used to calculate the potential solar irradiance that is
accumulated over the course of one day.
[MUSIC] Let’s move on now to
look at DEM-derived thematic variables that are specific to hydrology.
We will begin by introducing the concept of drainage lines.
A drainage line is theoretical path that a drop of water would
follow along the surface relief to reach the drainage basin.
In order to make the necessary calculations,
we assume that the surface is smooth and impermeable.
To begin the slope must be calculated for each pixel in the DEM.
Next,
we go to each high point within the catchment area
so those typically belong to the crest lines.
and then the drainage lines are determined gradually
by moving along the steepest surfaces.
From the generated flow lines,
we evaluate the drainage density.
This parameter is commonly used in hydrology.
It is equal to the total length of the fluvial networks per surface unit area.
Drainage density is correlated with the average flow rate,
mean annual precipitation or the rate of sediment production.
There are multiple different approaches that can use DEMs
in order to automatically delimit the surface area of a drainage basin.
These approaches can be divided into two categories:
the first of which identifies crestlines by beginning with fluvial outlets
and gradually moving to points located at higher elevations.
The second family of approaches also begin at the outlets,
but function by aggregating the highest neighbouring points together.
Gradually the lines converge towards the basin limits.
The principle underlying this approach appears logical and simple,
however results are often flawed if there are local minima, or errors can be created
in the presence in the presence of saddle-shaped land forms or along ridge-lines.
In 1991, Vincent and Soille proposed an alternative
method to avoid these problems;
we will describe the principle with a simple 1-dimensional
DEM made up of 10 elevation points.
DHere, the points or pixels are numbered from
1 to n depending on their initial order.
We then create a figure with the point number given by one axis
and the elevation given on the other.
In parallel, we also create an elevation frequency diagram with the numbered points.
In this example 2 pixels belong first elevation class, given in green,
5 are in the second class – these are the points in orange,
and 3 points make up the class with the highest elevation – these are the blue points.
The algorithm then
identifies the lowest point or points.
If they are isolated, they form the seed for one catchment.
So, in our example, pixel 1 is the lowest value.
It constitutes the first point of the catchment area given in blue.
LThe next lowest point – number 6 – is also a minimum
and it is selected next.
Because it is not located adjacent point 1
it forms the starting point for another catchment – this time given in red.
If no other points are found at the same elevation,
we move up an elevation level until we encounter a new point.
As point 2 is adjacent to a point that has already been allocated to the blue catchment area,
point 2 is also assigned to this basin.
We follow the same process until all of the pixels
have been assigned to a basin.
The system works as if the relief were progressively
inundated by groundwater until the entire study area is submerged.
When two basins join up their borders converge to form a ridge.
[MUSIC] We
will now demonstrate how these DEM-derived
variables can be applied in a real case.
All of the examples that we will present here
have been taken from an open access scientific publication,
in each of which, the applied methodologies
are described in detail.
The first example that we will review aims to identify floodable areas in Akure
a city in Nigeria with approximately 495,000 inhabitants.
The city has played a central role in the local political and economic climate,
and correspondingly has been the subject of significant population growth.
The rainy season in Akure lasts approximately 7 months,
between April and October, and experiences on average,
a yearly 1500mm of rainfall per square meter.
Because of the growing demand for building land,
the city is under intense pressure to identify potential flood zones
in order to ensure that they are not considered for future development.
In this study, Landsat satellite images were classified
to characterize the land use changes over the course of the study period
– from 2002 to 2011.
From these classified images, the authors were able to confirm that, in the region,
the built environment had undergone significant growth, while a decrease in riparian
vegetation has led to a reduction in the soil’s absorption capacity.
They also used a digital terrain model in their analysis; the DTM they used had a 90m spatial
resolution and was sourced from NASA’s Shuttle Radar Topography Mission, or SRTM.
Using the DTM, they calculated the slope of the study area,
and then they were able to generate the associated drainage lines.
Next, the authors integrated data on mean annual precipitation
that was collected at multiple different locations throughout the study area.
The different layers that were used in the analysis, like the land use map,
the drainage lines and the precipitation data were
weighted as a function of their probability of contributing to a flood.
Then a multicriteria approach was used to combine the layers and create a flood risk map
that evaluated the vulnerability of the study area according to three classes.
The second example that we will consider here evaluated the risk of rock falls.
The study area was situated in the Dades catchment located in Morocco
and consisted of an unstable zone that was sensitive to landslides.
The objective of the study was to create a map that illustrated the risk of rockfalls.
In order to create the map, a number of different criteria were considered.
First, a DTM with a spatial resolution of 90m was acquired from SRTM
and used to evaluate the surface slope throughout the study area.
From this, the authors confirmed that 30% of the study area
exhibited slopes that were
either steep or very steep.
In addition, the zone located uphill was strongly sloped and hilly,
which undoubtedly gives rise to the regions unstable character.
The authors also digitized geological maps in order to account for the lithology.
Indeed, the lithological composition and geologic structure play a strong role in
determining landslide risk.
Calcites and dolomites, represented in light blue on the map;
marly limestones, given in dark blue;
conglomerates and clays, in yellow,
are all sensitive to movement.
Based on the geological maps, the authors confirmed that 50% of the
study area is constituted of these highly sensitive materials.
The stability of rock formations is primarily controlled by the
density of tectonic fractures and their orientation.
As such, another map layer was created from field surveys and satellite imagery
in an effort to localize these features.
In this analysis, this is an especially important criterion because the High Atlas Mountains are in the
process of undergoing tectonic uplift and these fractures constitute an important destabilizing factor.
Information of the slope characteristics, the sensitivity to movement
and the degree of fracturing were combined in order to create a single risk map.
Each factor was divided into 5 qualitative classes that
ranged from “very low” to “very high”.
In the resultant risk map, each pixel is attributed by a value that is characterized
by a combination of the qualitative classes belonging to each of the three criteria.
The final map qualifies the hazard level according to 5 classes
that range from “very low risk” to “very high risk”.
The final result indicates that 25% of the catchment area is subject to
a very high level of risk.
[MUSIC] We will finish with an example taken
from the public health domain to illustrate that DEMs provide
invaluable information to a vast range of
applications.
This study on schistosomiasis was published in
2015 in PLOS Neglected Tropical Diseases.
Schistosomiasis is the most widely-spread water-borne
disease in all of Sub-Saharan Africa.
It is transmitted through freshwater snails
who act as intermediate hosts for the parasite.
The goal of the study was to identify potential habitats for the snail vector in order to
determine which zones were high risk areas for schistosomiasis transmission.
The study was conducted in a region in Burkina Faso, near Ouagadougou.
Multiple environmental variables were taken into account to delimit
potential vector habitats.
Among them were the ephemerality of stagnant water bodies,
which was calculated from RapidEye images
with a 6.5 satellite resolution
and from 30m resolution Landsat images
that were taken at different times.
Water temperature also influences snail mortality;
this was derived from the thermal band of the satellite images.
Vegetative cover was also analysed
as it can affect snail metabolism.
Finally,
using a 30m DEM sourced from Aster,
the slope of the surface was derived in order to estimate water depth and flow velocity.
An additive multicriteria function was then used
to weight and combine the different variables and
create an index that characterized an environment’s habitat potential.
This index was used to estimate the risk to determine
whether a specific zone was favorable to schistosomiasis transmission.
The redder the pixel is, the higher the risk of transmission.
A map such as this could prove very valuable for informing prevention
and control measures in order to limit the diffusion of the disease.
[MUSIC] In this lesson,
we presented a number of DEM-derived thematic variables
that can be calculated to supplement feasible analyses with the help of
measurements of slope, curvature and orientation.
Viewshed analyses in particular have found use in a range
of ecology-related applications, for example if we want to assess
the impact that a new construction will have on the surrounding landscape.
Further, given that renewable energies are gaining increasingly more support,
the possibility to calculate potential solar insolation
also constitutes an important variable.
DEMs also play an important role in hydrology,
and now you will now how you can use the to calculate
drainage lines and to delimit catchment areas.
To finish,
the examples that we present have also illustrated the potential
that DEMs have for creating effective prevention measures.
Floods, landslides, urban development or public health
there are a multitude of domains that can benefit from
information derived from DEMs.
And that is it for DEMs,
in the next lesson we will see how GIS can
be used to integrate different data types.
[MUSIC] [MUSIC]
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